Graphons, mergeons, and so on!
Authors: Justin Eldridge, Mikhail Belkin, Yusu Wang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work we develop a theory of hierarchical clustering for graphs. Our modeling assumption is that graphs are sampled from a graphon, which is a powerful and general model for generating graphs and analyzing large networks. Graphons are a far richer class of graph models than stochastic blockmodels, the primary setting for recent progress in the statistical theory of graph clustering. We define what it means for an algorithm to produce the correct" clustering, give sufficient conditions in which a method is statistically consistent, and provide an explicit algorithm satisfying these properties. Appendix F contains experiments in which the algorithm is applied to real and synthetic data. |
| Researcher Affiliation | Academia | Justin Eldridge Mikhail Belkin Yusu Wang The Ohio State University {eldridge, mbelkin, yusu}@cse.ohio-state.edu |
| Pseudocode | Yes | Algorithm 1 Clustering by nbhd. smoothing |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing open-source code for the described methodology. |
| Open Datasets | No | The paper mentions "real and synthetic data" in Appendix F for experiments, but it does not provide concrete access information (link, DOI, specific repository, or formal citation for the datasets themselves) for public availability. |
| Dataset Splits | No | The provided text does not include specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. Appendix F, where experiments are mentioned, is not accessible. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 1: Require: Adjacency matrix A, C (0, 1) % Step 1: Compute the estimated edge % probability matrix ˆP using neighborhood % smoothing algorithm based on [21] n Size(A) h C (log n)/n |